Abstraction and problem solving in an undergraduate electrical engineering circuits course

The ability to solve problems is a critical skill in all undergraduate engineering curricula. Students' capacity for problem solving is complicated by the fact that higher level reasoning skills, including the capacity for abstraction, are not innate in a person until the mid-twenties or later. The results of an exploratory study that looked at students' episodes of reasoning when solving problems in a sophomore level electrical circuits course are presented. Students' problem solving attempts are analyzed using the representation mapping framework developed by Hahn and Chater that is based on store representations of knowledge and how they are applied. This framework distinguishes between similarity and rules-based cognitive processes, and accounts for memory-bank, rules-based, similarity-based and prototype types of reasoning. Students were asked to think aloud when solving specific problems selected by the course instructor. The interviews were recorded, transcribed, and analyzed in detail to identify the types of reasoning and the degree of abstraction in the students' problem solving attempts. This study demonstrated that representation mapping is useful framework for studying students' problem solving skills in electrical engineering.

[1]  Ann M. L. Cavallo,et al.  Meaningful learning, reasoning ability, and students' understanding and problem solving of topics in genetics , 1996 .

[2]  Lisa Benson,et al.  A coding scheme for analysing problem-solving processes of first-year engineering students , 2014 .

[3]  Richard M. Felder,et al.  THE FUTURE OF ENGINEERING EDUCATION I. A VISION FOR A NEW CENTURY , 2000 .

[4]  D. Kolb.  Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[5]  Kathryn W. Jablokow,et al.  Engineers as Problem-Solving Leaders: Embracing the Humanities , 2007, IEEE Technology and Society Magazine.

[6]  Phillip J. Cornwell,et al.  Integrating Mechanics throughout the Sophomore Year , 1998 .

[7]  George M. Bodner,et al.  Using students' representations constructed during problem solving to infer conceptual understanding , 2012 .

[8]  N. Chater,et al.  Similarity and rules: distinct? exhaustive? empirically distinguishable? , 1998, Cognition.

[9]  Xiaoying Xu,et al.  Comparing Two Tests of Formal Reasoning in a College Chemistry Context , 2010 .

[10]  B. Dougherty,et al.  Defining expectations for problem-solving skills , 1996 .

[11]  John Sweller,et al.  Cognitive Load During Problem Solving: Effects on Learning , 1988, Cogn. Sci..

[12]  Qurban A. Memon,et al.  On analysis of electrical engineering programme in GCC countries , 2007 .

[13]  Meredith D. Gall,et al.  Educational Research: An Introduction , 1965 .

[14]  Mansoor Niaz,et al.  Reasoning strategies of students in solving chemistry problems as a function of developmental level, functional M‐capacity and disembedding ability , 1996 .

[15]  Carlos Domínguez,et al.  Innovative methodology to improve the quality of electronic engineering formation through teaching industrial computer engineering , 2004, IEEE Transactions on Education.

[16]  Leonie Kohl,et al.  Constructivism And The Technology Of Instruction A Conversation , 2016 .

[17]  Paul J. Feltovich,et al.  Categorization and Representation of Physics Problems by Experts and Novices , 1981, Cogn. Sci..

[18]  M. Frank,et al.  Characteristics of engineering systems thinking - a 3D approach for curriculum content , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[19]  Dorothea P. Simon,et al.  Expert and Novice Performance in Solving Physics Problems , 1980, Science.